Fall detection addresses an important public health problem, especially among the elderly. Traditionally, fall detection applications are broadly categorized into two types: context-aware systems and wearable devices. The context-aware systems use sensors such as cameras, floor sensors and microphones deployed in certain environment to detect falls of people who enter the monitored environment. The wearable devices are miniature electronic sensor-based devices worn by a bearer/subject under, with or on top of clothing. The vast majority of wearable fall detectors adopt the form of accelerometer devices, or a combination of accelerometer and other sensors such as a tilt sensor or gyroscopes to obtain information about the position of the subject.
A common feature of both types of fall detection applications is the reliance on mechanical signals of the movement or change of movement to assess the occurrence of a fall event. A major factor limiting the usefulness of existing motion-based fall detection applications is high rate of false alerts, which are resulted from poor performance of the devices in discriminating normal living activities from true fall events.
The disclosed method and system are directed to solve one or more problems set forth above and other problems.